importing libraries
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
links for covid 19 dataset
covid19_confirmed_cases_link = "time_series_covid19_confirmed_global.csv"
covid19_death_cases_link = "time_series_covid19_deaths_global.csv"
covid19_recovered_cases_link = "time_series_covid19_recovered_global.csv"
covid19_countries_cases_link = "cases_country.csv"
downloading dataset in respective dataframes
confirmed_df = pd.read_csv(covid19_confirmed_cases_link)
death_df = pd.read_csv(covid19_death_cases_link)
recovered_df = pd.read_csv(covid19_recovered_cases_link)
cases_countries_df = pd.read_csv(covid19_countries_cases_link)
print(confirmed_df.shape)
print(death_df.shape)
print(recovered_df.shape)
print(cases_countries_df.shape)
(266, 209) (266, 209) (253, 209) (188, 14)
it shows 266 rows and as we can see below their is a column country their so we can simply conclude that each row cannot be a country.
confirmed_df[confirmed_df["Country/Region"] == "Australia"]
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | Australian Capital Territory | Australia | -35.4735 | 149.0124 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 | 113 |
| 9 | New South Wales | Australia | -33.8688 | 151.2093 | 0 | 0 | 0 | 0 | 3 | 4 | ... | 3820 | 3832 | 3842 | 3851 | 3861 | 3875 | 3897 | 3915 | 3927 | 3936 |
| 10 | Northern Territory | Australia | -12.4634 | 130.8456 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 33 | 33 | 33 | 33 | 33 | 33 | 33 | 33 | 33 | 33 |
| 11 | Queensland | Australia | -27.4698 | 153.0251 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1088 | 1088 | 1087 | 1088 | 1088 | 1089 | 1089 | 1089 | 1089 | 1091 |
| 12 | South Australia | Australia | -34.9285 | 138.6007 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 457 | 456 | 459 | 459 | 459 | 459 | 459 | 459 | 459 | 460 |
| 13 | Tasmania | Australia | -42.8821 | 147.3272 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 229 | 229 | 229 | 229 | 229 | 229 | 229 | 230 | 230 | 230 |
| 14 | Victoria | Australia | -37.8136 | 144.9631 | 0 | 0 | 0 | 0 | 1 | 1 | ... | 13035 | 13469 | 13867 | 14283 | 14659 | 14957 | 15251 | 15646 | 15863 | 16234 |
| 15 | Western Australia | Australia | -31.9505 | 115.8605 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 670 | 670 | 642 | 642 | 642 | 642 | 642 | 642 | 644 | 645 |
8 rows × 209 columns
so to find out how many diffrent countries are their we want to use nunique function.
confirmed_df["Country/Region"].nunique()
188
so this data contains 188 diffrent countries .
now as their might be many null values so we are going to imputing data.
confirmed_df = confirmed_df.replace(np.nan, '', regex = True)
death_df = death_df.replace(np.nan, '', regex = True)
recovered_df = recovered_df.replace(np.nan, '', regex = True)
cases_countries_df = cases_countries_df.replace(np.nan, '', regex = True)
confirmed_df
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 8/4/20 | 8/5/20 | 8/6/20 | 8/7/20 | 8/8/20 | 8/9/20 | 8/10/20 | 8/11/20 | 8/12/20 | 8/13/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 33.939110 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 36782 | 36829 | 36896 | 37015 | 37054 | 37054 | 37162 | 37269 | 37345 | 37424 | |
| 1 | Albania | 41.153300 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 5750 | 5889 | 6016 | 6151 | 6275 | 6411 | 6536 | 6676 | 6817 | 6971 | |
| 2 | Algeria | 28.033900 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 32504 | 33055 | 33626 | 34155 | 34693 | 35160 | 35712 | 36204 | 36699 | 37187 | |
| 3 | Andorra | 42.506300 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 939 | 939 | 944 | 955 | 955 | 955 | 963 | 963 | 977 | 981 | |
| 4 | Angola | -11.202700 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1344 | 1395 | 1483 | 1538 | 1572 | 1672 | 1679 | 1735 | 1762 | 1815 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 261 | West Bank and Gaza | 31.952200 | 35.233200 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 12770 | 13065 | 13398 | 13722 | 13928 | 14208 | 14510 | 14875 | 15184 | 15491 | |
| 262 | Western Sahara | 24.215500 | -12.885800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | |
| 263 | Yemen | 15.552727 | 48.516388 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1760 | 1763 | 1768 | 1796 | 1797 | 1804 | 1832 | 1831 | 1841 | 1847 | |
| 264 | Zambia | -13.133897 | 27.849332 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6793 | 7022 | 7164 | 7486 | 7903 | 8085 | 8210 | 8275 | 8501 | 8663 | |
| 265 | Zimbabwe | -19.015438 | 29.154857 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 4221 | 4221 | 4339 | 4451 | 4575 | 4649 | 4748 | 4818 | 4893 | 4990 |
266 rows × 209 columns
confirmed_df.columns
Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',
'1/24/20', '1/25/20', '1/26/20', '1/27/20',
...
'8/4/20', '8/5/20', '8/6/20', '8/7/20', '8/8/20', '8/9/20', '8/10/20',
'8/11/20', '8/12/20', '8/13/20'],
dtype='object', length=209)
cases_countries_df
| Country_Region | Last_Update | Lat | Long_ | Confirmed | Deaths | Recovered | Active | Incident_Rate | People_Tested | People_Hospitalized | Mortality_Rate | UID | ISO3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 2020-08-14 11:27:27 | 33.9391 | 67.71 | 37431.0 | 1363.0 | 26714 | 9354.0 | 96.1536 | 3.641367 | 4 | AFG | ||
| 1 | Albania | 2020-08-14 11:27:27 | 41.1533 | 20.1683 | 6971.0 | 213.0 | 3616 | 3142.0 | 242.234 | 3.055516 | 8 | ALB | ||
| 2 | Algeria | 2020-08-14 11:27:27 | 28.0339 | 1.6596 | 37187.0 | 1341.0 | 26004 | 9842.0 | 84.803 | 3.606099 | 12 | DZA | ||
| 3 | Andorra | 2020-08-14 11:27:27 | 42.5063 | 1.5218 | 981.0 | 53.0 | 858 | 70.0 | 1269.66 | 5.402650 | 20 | AND | ||
| 4 | Angola | 2020-08-14 11:27:27 | -11.2027 | 17.8739 | 1815.0 | 80.0 | 577 | 1158.0 | 5.52238 | 4.407713 | 24 | AGO | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 183 | West Bank and Gaza | 2020-08-14 11:27:27 | 31.9522 | 35.2332 | 15834.0 | 106.0 | 9382 | 6346.0 | 310.384 | 0.669445 | 275 | PSE | ||
| 184 | Western Sahara | 2020-08-14 11:27:27 | 24.2155 | -12.8858 | 10.0 | 1.0 | 8 | 1.0 | 1.67412 | 10.000000 | 732 | ESH | ||
| 185 | Yemen | 2020-08-14 11:27:27 | 15.5527 | 48.5164 | 1847.0 | 528.0 | 949 | 370.0 | 6.19259 | 28.586898 | 887 | YEM | ||
| 186 | Zambia | 2020-08-14 11:27:27 | -13.1339 | 27.8493 | 8663.0 | 246.0 | 7401 | 1016.0 | 47.1226 | 2.839663 | 894 | ZMB | ||
| 187 | Zimbabwe | 2020-08-14 11:27:27 | -19.0154 | 29.1549 | 4990.0 | 128.0 | 1927 | 2935.0 | 33.5735 | 2.565130 | 716 | ZWE |
188 rows × 14 columns
first three are time series data and the fourth one "case_countries_df" is the aggregated data . so , to find out total number of cases we use the fourth_one by adding all the patients whether they are confirmed ,or they dead , or they active or recovered .
global_data = cases_countries_df.copy().drop(['Country_Region', 'Last_Update', 'Lat', 'Long_', 'Incident_Rate', 'People_Tested', 'People_Hospitalized', 'Mortality_Rate', 'UID' , 'ISO3'], axis=1)
global_summary = pd.DataFrame(global_data.sum()).transpose()
global_summary.style.format("{:0.0f}")
| Confirmed | Deaths | Active | |
|---|---|---|---|
| 0 | 20950402 | 760213 | 7175587 |
to get a plot we want the total number of confirmed cases globally.
confirmed_ts = confirmed_df.copy().drop(['Lat', 'Long','Province/State', 'Country/Region'], axis=1)
confirmed_ts_summary = confirmed_ts.sum()
confirmed_ts_summary
1/22/20 555
1/23/20 654
1/24/20 941
1/25/20 1434
1/26/20 2118
...
8/9/20 19861683
8/10/20 20089624
8/11/20 20344188
8/12/20 20621140
8/13/20 20905891
Length: 205, dtype: int64
fig_1 = go.Figure(data=go.Scatter(x=confirmed_ts_summary.index, y=confirmed_ts_summary.values,mode = 'lines+markers'))
fig_1.update_layout(title='Total confirmed coronavirus cases globally', yaxis_title = 'confirmed_cases', xaxis_tickangle = 300)
fig_1.show()
now as we are going to make the graph for all the four diffrent parameters that is active , recovered , death and confirmed we will make an template function so that it is easy for us and we can use it again and again with color array.
#here we are initializing a color array
color_arr = px.colors.qualitative.Dark24
def draw_plot(ts_array, ts_label, title, colors, mode_size,line_size, x_axis_title, y_axis_title, tickangle = 0, y_axis_type=''):
#initilalizing figure
fig = go.Figure()
#adding all traces
for index, ts in enumerate(ts_array):
fig.add_trace(go.Scatter(x=ts.index,
y=ts.values,
name = ts_label[index],
line=dict(color=colors[index], width=line_size[index]),connectgaps = True),)
#base x_axis properties
x_axis_dict = dict(showline=True,
showgrid=True,
showticklabels=True,
linecolor='rgb(200,200,200)',
linewidth=2,
ticks='outside',
tickfont=dict(family='Arial', size=12, color='rgb(90,90,90)',))
# x_axis parameters
if x_axis_title:
x_axis_dict['title'] = x_axis_title
if tickangle > 0:
x_axis_dict['tickangle'] =tickangle
#y_axis properties
y_axis_dict = dict(showline=True,
showgrid=True,
showticklabels=True,
linecolor='rgb(200,200,200)',
linewidth=2,)
#y_axis parameters
if y_axis_type != "":
y_axis_dict['type'] = y_axis_type
if y_axis_title:
y_axis_dict['title'] = y_axis_title
#updating the layout
fig.update_layout(xaxis = x_axis_dict,
yaxis = y_axis_dict,
autosize=True,
margin=dict(autoexpand=True,l=100,r=20, t=110,),
showlegend =True,
)
#annotations for any basic graph
annotations = []
#title
annotations.append(dict(xref='paper', yref='paper', x=0.0, y= 1.05, xanchor='left', yanchor='bottom',text=title,
font=dict(family='Arial' ,size= 16, color='rgb(40,40,40)'),showarrow=False))
# #adding annotationsi params
# if len(additional_annotations) > 0:
# annotations.append(additional_annotations)
# #updating the layout
# fig.update_layout(annotations=annotations)
return fig
confirmed_agg_ts = confirmed_df.copy().drop(['Lat', 'Long','Province/State', 'Country/Region'],axis=1).sum()
death_agg_ts = death_df.copy().drop(['Lat', 'Long','Province/State', 'Country/Region'],axis=1).sum()
recovered_agg_ts = recovered_df.copy().drop(['Lat', 'Long','Province/State', 'Country/Region'],axis=1).sum()
#now as thier is no timeseries data for the active cases therefore we calculated it simply by list comprehension
active_agg_ts =pd.Series(
data=np.array(
[x1-x2-x3 for (x1,x2,x3) in zip(confirmed_agg_ts.values,death_agg_ts.values,recovered_agg_ts.values)]),
index=confirmed_agg_ts.index)
ts_array = [confirmed_agg_ts, active_agg_ts, recovered_agg_ts, death_agg_ts]
labels = ['Confirmed', 'Active', 'Recovered', 'Deaths']
colors = [color_arr[5], color_arr[0], color_arr[2], color_arr[3]]
mode_size = [8,8,12,8]
line_size = [2,2,4,2]
#now we call the draw_plot function that we defined above
fig_2 = draw_plot(ts_array = ts_array,
ts_label = labels,
title = "Covid 19 cases status from 22nd jan to 9 august 2020",
colors = colors, mode_size = mode_size,
line_size = line_size,
x_axis_title = "Date",
y_axis_title = "case-count",
tickangle = 300,
y_axis_type = '')
fig_2.show()
cases_countries_df
| Country_Region | Last_Update | Lat | Long_ | Confirmed | Deaths | Recovered | Active | Incident_Rate | People_Tested | People_Hospitalized | Mortality_Rate | UID | ISO3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 2020-08-14 11:27:27 | 33.9391 | 67.71 | 37431.0 | 1363.0 | 26714 | 9354.0 | 96.1536 | 3.641367 | 4 | AFG | ||
| 1 | Albania | 2020-08-14 11:27:27 | 41.1533 | 20.1683 | 6971.0 | 213.0 | 3616 | 3142.0 | 242.234 | 3.055516 | 8 | ALB | ||
| 2 | Algeria | 2020-08-14 11:27:27 | 28.0339 | 1.6596 | 37187.0 | 1341.0 | 26004 | 9842.0 | 84.803 | 3.606099 | 12 | DZA | ||
| 3 | Andorra | 2020-08-14 11:27:27 | 42.5063 | 1.5218 | 981.0 | 53.0 | 858 | 70.0 | 1269.66 | 5.402650 | 20 | AND | ||
| 4 | Angola | 2020-08-14 11:27:27 | -11.2027 | 17.8739 | 1815.0 | 80.0 | 577 | 1158.0 | 5.52238 | 4.407713 | 24 | AGO | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 183 | West Bank and Gaza | 2020-08-14 11:27:27 | 31.9522 | 35.2332 | 15834.0 | 106.0 | 9382 | 6346.0 | 310.384 | 0.669445 | 275 | PSE | ||
| 184 | Western Sahara | 2020-08-14 11:27:27 | 24.2155 | -12.8858 | 10.0 | 1.0 | 8 | 1.0 | 1.67412 | 10.000000 | 732 | ESH | ||
| 185 | Yemen | 2020-08-14 11:27:27 | 15.5527 | 48.5164 | 1847.0 | 528.0 | 949 | 370.0 | 6.19259 | 28.586898 | 887 | YEM | ||
| 186 | Zambia | 2020-08-14 11:27:27 | -13.1339 | 27.8493 | 8663.0 | 246.0 | 7401 | 1016.0 | 47.1226 | 2.839663 | 894 | ZMB | ||
| 187 | Zimbabwe | 2020-08-14 11:27:27 | -19.0154 | 29.1549 | 4990.0 | 128.0 | 1927 | 2935.0 | 33.5735 | 2.565130 | 716 | ZWE |
188 rows × 14 columns
now we are working at country level
cases_countries_df.copy().drop(
['Lat', 'Long_','Last_Update'],axis = 1).sort_values('Confirmed', ascending = False).reset_index(drop=True).style.bar(
align = "left", width=198, color= '#ff781c')
| Country_Region | Confirmed | Deaths | Recovered | Active | Incident_Rate | People_Tested | People_Hospitalized | Mortality_Rate | UID | ISO3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | US | 5254878.000000 | 167253.000000 | 1774648.000000 | 3312981.000000 | 1594.966851 | 3.182814 | 840 | USA | ||
| 1 | Brazil | 3224876.000000 | 105463.000000 | 2521100.000000 | 598313.000000 | 1517.164550 | 3.270296 | 76 | BRA | ||
| 2 | India | 2461190.000000 | 48040.000000 | 1751555.000000 | 661595.000000 | 178.346535 | 1.951901 | 356 | IND | ||
| 3 | Russia | 910778.000000 | 15467.000000 | 721473.000000 | 173838.000000 | 624.100709 | 1.698218 | 643 | RUS | ||
| 4 | South Africa | 572865.000000 | 11270.000000 | 437617.000000 | 123978.000000 | 965.903985 | 1.967305 | 710 | ZAF | ||
| 5 | Peru | 507996.000000 | 25648.000000 | 341938.000000 | 140410.000000 | 1540.696266 | 5.048859 | 604 | PER | ||
| 6 | Mexico | 505751.000000 | 55293.000000 | 406583.000000 | 43875.000000 | 395.760195 | 10.932850 | 484 | MEX | ||
| 7 | Colombia | 433805.000000 | 14145.000000 | 250494.000000 | 169166.000000 | 852.555842 | 3.260682 | 170 | COL | ||
| 8 | Chile | 380034.000000 | 10299.000000 | 353131.000000 | 16604.000000 | 1988.019696 | 2.710021 | 152 | CHL | ||
| 9 | Iran | 338825.000000 | 19331.000000 | 293811.000000 | 25683.000000 | 403.396937 | 5.705305 | 364 | IRN | ||
| 10 | Spain | 337334.000000 | 28605.000000 | 150376.000000 | 158353.000000 | 721.496237 | 8.479726 | 724 | ESP | ||
| 11 | United Kingdom | 315600.000000 | 46791.000000 | 1479.000000 | 267330.000000 | 464.897006 | 14.826046 | 826 | GBR | ||
| 12 | Saudi Arabia | 294519.000000 | 3303.000000 | 260393.000000 | 30823.000000 | 845.981861 | 1.121490 | 682 | SAU | ||
| 13 | Pakistan | 287300.000000 | 6153.000000 | 265215.000000 | 15932.000000 | 130.063366 | 2.141664 | 586 | PAK | ||
| 14 | Argentina | 276072.000000 | 5362.000000 | 192434.000000 | 78276.000000 | 610.835831 | 1.942247 | 32 | ARG | ||
| 15 | Bangladesh | 271881.000000 | 3591.000000 | 156623.000000 | 111667.000000 | 165.087145 | 1.320798 | 50 | BGD | ||
| 16 | Italy | 252235.000000 | 35231.000000 | 202923.000000 | 14081.000000 | 417.180572 | 13.967530 | 380 | ITA | ||
| 17 | Turkey | 245635.000000 | 5912.000000 | 228057.000000 | 11666.000000 | 291.246997 | 2.406823 | 792 | TUR | ||
| 18 | France | 244096.000000 | 30392.000000 | 83612.000000 | 130092.000000 | 373.958735 | 12.450839 | 250 | FRA | ||
| 19 | Germany | 222557.000000 | 9229.000000 | 200266.000000 | 13062.000000 | 265.632037 | 4.146803 | 276 | DEU | ||
| 20 | Iraq | 164277.000000 | 5641.000000 | 117208.000000 | 41428.000000 | 408.420630 | 3.433834 | 368 | IRQ | ||
| 21 | Philippines | 153660.000000 | 2442.000000 | 71405.000000 | 79813.000000 | 140.224930 | 1.589223 | 608 | PHL | ||
| 22 | Indonesia | 135123.000000 | 6021.000000 | 89618.000000 | 39484.000000 | 49.400852 | 4.455940 | 360 | IDN | ||
| 23 | Canada | 123184.000000 | 9063.000000 | 109260.000000 | 4861.000000 | 325.404083 | 7.357287 | 124 | CAN | ||
| 24 | Qatar | 114281.000000 | 190.000000 | 110957.000000 | 3134.000000 | 3966.630337 | 0.166257 | 634 | QAT | ||
| 25 | Kazakhstan | 101848.000000 | 1269.000000 | 78633.000000 | 21946.000000 | 542.416729 | 1.245974 | 398 | KAZ | ||
| 26 | Ecuador | 98343.000000 | 6010.000000 | 78957.000000 | 13376.000000 | 557.403308 | 6.111264 | 218 | ECU | ||
| 27 | Bolivia | 96459.000000 | 3884.000000 | 33720.000000 | 58855.000000 | 826.340790 | 4.026581 | 68 | BOL | ||
| 28 | Egypt | 96108.000000 | 5107.000000 | 56890.000000 | 34111.000000 | 93.915631 | 5.313814 | 818 | EGY | ||
| 29 | Israel | 90472.000000 | 657.000000 | 66151.000000 | 23664.000000 | 1045.249511 | 0.726192 | 376 | ISR | ||
| 30 | Ukraine | 89917.000000 | 2042.000000 | 48288.000000 | 39587.000000 | 205.600895 | 2.270983 | 804 | UKR | ||
| 31 | China | 89192.000000 | 4700.000000 | 82901.000000 | 1591.000000 | 6.349648 | 5.269531 | 156 | CHN | ||
| 32 | Sweden | 83852.000000 | 5776.000000 | 78076.000000 | 830.277832 | 6.888327 | 752 | SWE | |||
| 33 | Dominican Republic | 83134.000000 | 1393.000000 | 47946.000000 | 33795.000000 | 766.360027 | 1.675608 | 214 | DOM | ||
| 34 | Oman | 82531.000000 | 551.000000 | 77278.000000 | 4702.000000 | 1616.156434 | 0.667628 | 512 | OMN | ||
| 35 | Panama | 78446.000000 | 1722.000000 | 52210.000000 | 24514.000000 | 1818.081528 | 2.195141 | 591 | PAN | ||
| 36 | Belgium | 76191.000000 | 9916.000000 | 17913.000000 | 48362.000000 | 657.407459 | 13.014661 | 56 | BEL | ||
| 37 | Kuwait | 74486.000000 | 489.000000 | 66099.000000 | 7898.000000 | 1744.172841 | 0.656499 | 414 | KWT | ||
| 38 | Belarus | 69203.000000 | 599.000000 | 66178.000000 | 2426.000000 | 732.359500 | 0.865569 | 112 | BLR | ||
| 39 | Romania | 68046.000000 | 2904.000000 | 31920.000000 | 33222.000000 | 353.712053 | 4.267701 | 642 | ROU | ||
| 40 | United Arab Emirates | 63489.000000 | 358.000000 | 57372.000000 | 5759.000000 | 641.925504 | 0.563877 | 784 | ARE | ||
| 41 | Netherlands | 62406.000000 | 6187.000000 | 253.000000 | 55966.000000 | 364.204625 | 9.914111 | 528 | NLD | ||
| 42 | Guatemala | 60284.000000 | 2296.000000 | 48305.000000 | 9683.000000 | 336.489490 | 3.808639 | 320 | GTM | ||
| 43 | Singapore | 55580.000000 | 27.000000 | 50736.000000 | 4817.000000 | 950.029767 | 0.048579 | 702 | SGP | ||
| 44 | Poland | 55319.000000 | 1858.000000 | 38362.000000 | 15099.000000 | 146.166347 | 3.358701 | 616 | POL | ||
| 45 | Portugal | 53548.000000 | 1770.000000 | 39177.000000 | 12601.000000 | 525.149933 | 3.305446 | 620 | PRT | ||
| 46 | Japan | 53318.000000 | 1074.000000 | 36191.000000 | 16053.000000 | 42.156462 | 2.014329 | 392 | JPN | ||
| 47 | Honduras | 49042.000000 | 1542.000000 | 7032.000000 | 40468.000000 | 495.143271 | 3.144244 | 340 | HND | ||
| 48 | Nigeria | 48116.000000 | 966.000000 | 34309.000000 | 12841.000000 | 23.341465 | 2.007648 | 566 | NGA | ||
| 49 | Bahrain | 45726.000000 | 167.000000 | 42180.000000 | 3379.000000 | 2687.262390 | 0.365219 | 48 | BHR | ||
| 50 | Ghana | 41725.000000 | 223.000000 | 39495.000000 | 2007.000000 | 134.280803 | 0.534452 | 288 | GHA | ||
| 51 | Kyrgyzstan | 41373.000000 | 1491.000000 | 33592.000000 | 6290.000000 | 634.147590 | 3.603800 | 417 | KGZ | ||
| 52 | Armenia | 41299.000000 | 814.000000 | 34164.000000 | 6321.000000 | 1393.713760 | 1.970992 | 51 | ARM | ||
| 53 | Morocco | 37935.000000 | 584.000000 | 26687.000000 | 10664.000000 | 102.775471 | 1.539475 | 504 | MAR | ||
| 54 | Switzerland | 37671.000000 | 1991.000000 | 32700.000000 | 2980.000000 | 435.270511 | 5.285233 | 756 | CHE | ||
| 55 | Afghanistan | 37431.000000 | 1363.000000 | 26714.000000 | 9354.000000 | 96.153597 | 3.641367 | 4 | AFG | ||
| 56 | Algeria | 37187.000000 | 1341.000000 | 26004.000000 | 9842.000000 | 84.803000 | 3.606099 | 12 | DZA | ||
| 57 | Azerbaijan | 33915.000000 | 500.000000 | 31269.000000 | 2146.000000 | 334.494670 | 1.474274 | 31 | AZE | ||
| 58 | Uzbekistan | 33561.000000 | 218.000000 | 27213.000000 | 6130.000000 | 100.274285 | 0.649563 | 860 | UZB | ||
| 59 | Venezuela | 30369.000000 | 259.000000 | 21385.000000 | 8725.000000 | 106.797935 | 0.852843 | 862 | VEN | ||
| 60 | Moldova | 29087.000000 | 878.000000 | 20276.000000 | 7933.000000 | 721.052722 | 3.018531 | 498 | MDA | ||
| 61 | Serbia | 28998.000000 | 661.000000 | 28337.000000 | 331.884766 | 2.279468 | 688 | SRB | |||
| 62 | Kenya | 28754.000000 | 460.000000 | 15100.000000 | 13194.000000 | 53.474623 | 1.599777 | 404 | KEN | ||
| 63 | Ireland | 26929.000000 | 1774.000000 | 23364.000000 | 1791.000000 | 545.364774 | 6.587694 | 372 | IRL | ||
| 64 | Ethiopia | 26204.000000 | 479.000000 | 11428.000000 | 14297.000000 | 22.793305 | 1.827965 | 231 | ETH | ||
| 65 | Costa Rica | 26129.000000 | 272.000000 | 8412.000000 | 17445.000000 | 512.925309 | 1.040989 | 188 | CRI | ||
| 66 | Nepal | 25551.000000 | 99.000000 | 17077.000000 | 8375.000000 | 87.693202 | 0.387460 | 524 | NPL | ||
| 67 | Austria | 22876.000000 | 725.000000 | 20499.000000 | 1652.000000 | 253.997158 | 3.169260 | 40 | AUT | ||
| 68 | Australia | 22743.000000 | 375.000000 | 13350.000000 | 9018.000000 | 89.329411 | 1.648859 | 36 | AUS | ||
| 69 | El Salvador | 22314.000000 | 595.000000 | 10455.000000 | 11264.000000 | 344.022641 | 2.666487 | 222 | SLV | ||
| 70 | Czechia | 19401.000000 | 391.000000 | 13574.000000 | 5436.000000 | 181.165679 | 2.015360 | 203 | CZE | ||
| 71 | Cameroon | 18308.000000 | 401.000000 | 15320.000000 | 2587.000000 | 68.967429 | 2.190299 | 120 | CMR | ||
| 72 | Cote d'Ivoire | 16889.000000 | 107.000000 | 13522.000000 | 3260.000000 | 64.026173 | 0.633548 | 384 | CIV | ||
| 73 | West Bank and Gaza | 15834.000000 | 106.000000 | 9382.000000 | 6346.000000 | 310.384411 | 0.669445 | 275 | PSE | ||
| 74 | Denmark | 15590.000000 | 621.000000 | 13370.000000 | 1599.000000 | 269.154931 | 3.983323 | 208 | DNK | ||
| 75 | Bosnia and Herzegovina | 15535.000000 | 469.000000 | 9344.000000 | 5722.000000 | 473.510393 | 3.018989 | 70 | BIH | ||
| 76 | Korea, South | 14873.000000 | 305.000000 | 13863.000000 | 705.000000 | 29.009629 | 2.050696 | 410 | KOR | ||
| 77 | Bulgaria | 14069.000000 | 484.000000 | 8901.000000 | 4684.000000 | 202.476957 | 3.440188 | 100 | BGR | ||
| 78 | Madagascar | 13643.000000 | 164.000000 | 12011.000000 | 1468.000000 | 49.268682 | 1.202082 | 450 | MDG | ||
| 79 | North Macedonia | 12357.000000 | 532.000000 | 8662.000000 | 3163.000000 | 593.122714 | 4.305252 | 807 | MKD | ||
| 80 | Sudan | 12115.000000 | 792.000000 | 6305.000000 | 5018.000000 | 27.628739 | 6.537350 | 729 | SDN | ||
| 81 | Senegal | 11872.000000 | 249.000000 | 7615.000000 | 4008.000000 | 70.903306 | 2.097372 | 686 | SEN | ||
| 82 | Kosovo | 10795.000000 | 365.000000 | 6411.000000 | 4019.000000 | 596.288264 | 3.381195 | 383 | XKS | ||
| 83 | Norway | 9851.000000 | 257.000000 | 8857.000000 | 737.000000 | 181.711128 | 2.608872 | 578 | NOR | ||
| 84 | Congo (Kinshasa) | 9605.000000 | 238.000000 | 8512.000000 | 855.000000 | 10.724486 | 2.477876 | 180 | COD | ||
| 85 | Malaysia | 9149.000000 | 125.000000 | 8828.000000 | 196.000000 | 28.267319 | 1.366270 | 458 | MYS | ||
| 86 | Zambia | 8663.000000 | 246.000000 | 7401.000000 | 1016.000000 | 47.122611 | 2.839663 | 894 | ZMB | ||
| 87 | Paraguay | 8389.000000 | 97.000000 | 5516.000000 | 2776.000000 | 117.616049 | 1.156276 | 600 | PRY | ||
| 88 | Guinea | 8198.000000 | 50.000000 | 7120.000000 | 1028.000000 | 62.423893 | 0.609905 | 324 | GIN | ||
| 89 | Gabon | 8077.000000 | 51.000000 | 5920.000000 | 2106.000000 | 362.892501 | 0.631423 | 266 | GAB | ||
| 90 | Tajikistan | 7950.000000 | 63.000000 | 6741.000000 | 1146.000000 | 83.353936 | 0.792453 | 762 | TJK | ||
| 91 | Haiti | 7810.000000 | 192.000000 | 5123.000000 | 2495.000000 | 68.493553 | 2.458387 | 332 | HTI | ||
| 92 | Lebanon | 7711.000000 | 92.000000 | 2496.000000 | 5123.000000 | 112.974369 | 1.193101 | 422 | LBN | ||
| 93 | Finland | 7700.000000 | 333.000000 | 7050.000000 | 317.000000 | 138.971159 | 4.324675 | 246 | FIN | ||
| 94 | Luxembourg | 7368.000000 | 122.000000 | 6414.000000 | 832.000000 | 1177.041931 | 1.655809 | 442 | LUX | ||
| 95 | Libya | 7050.000000 | 135.000000 | 816.000000 | 6099.000000 | 102.600866 | 1.914894 | 434 | LBY | ||
| 96 | Albania | 6971.000000 | 213.000000 | 3616.000000 | 3142.000000 | 242.233651 | 3.055516 | 8 | ALB | ||
| 97 | Mauritania | 6653.000000 | 157.000000 | 5843.000000 | 653.000000 | 143.085731 | 2.359838 | 478 | MRT | ||
| 98 | Greece | 6381.000000 | 221.000000 | 1347.000000 | 4813.000000 | 61.220049 | 3.463407 | 300 | GRC | ||
| 99 | Croatia | 6050.000000 | 161.000000 | 5078.000000 | 811.000000 | 147.371621 | 2.661157 | 191 | HRV | ||
| 100 | Maldives | 5494.000000 | 21.000000 | 2920.000000 | 2553.000000 | 1016.387256 | 0.382235 | 462 | MDV | ||
| 101 | Djibouti | 5358.000000 | 59.000000 | 5167.000000 | 132.000000 | 542.306595 | 1.101157 | 262 | DJI | ||
| 102 | Zimbabwe | 4990.000000 | 128.000000 | 1927.000000 | 2935.000000 | 33.573468 | 2.565130 | 716 | ZWE | ||
| 103 | Malawi | 4912.000000 | 153.000000 | 2550.000000 | 2209.000000 | 25.677008 | 3.114821 | 454 | MWI | ||
| 104 | Hungary | 4853.000000 | 607.000000 | 3590.000000 | 656.000000 | 50.236275 | 12.507727 | 348 | HUN | ||
| 105 | Equatorial Guinea | 4821.000000 | 83.000000 | 2182.000000 | 2556.000000 | 343.624486 | 1.721635 | 226 | GNQ | ||
| 106 | Central African Republic | 4652.000000 | 61.000000 | 1728.000000 | 2863.000000 | 96.319406 | 1.311264 | 140 | CAF | ||
| 107 | Nicaragua | 4115.000000 | 128.000000 | 2913.000000 | 1074.000000 | 62.117389 | 3.110571 | 558 | NIC | ||
| 108 | Montenegro | 3857.000000 | 73.000000 | 2680.000000 | 1104.000000 | 614.111346 | 1.892663 | 499 | MNE | ||
| 109 | Congo (Brazzaville) | 3745.000000 | 60.000000 | 1625.000000 | 2060.000000 | 67.867662 | 1.602136 | 178 | COG | ||
| 110 | Eswatini | 3599.000000 | 65.000000 | 1991.000000 | 1543.000000 | 310.214763 | 1.806057 | 748 | SWZ | ||
| 111 | Namibia | 3544.000000 | 27.000000 | 848.000000 | 2669.000000 | 139.477259 | 0.761851 | 516 | NAM | ||
| 112 | Thailand | 3376.000000 | 58.000000 | 3173.000000 | 145.000000 | 4.836678 | 1.718009 | 764 | THA | ||
| 113 | Somalia | 3227.000000 | 93.000000 | 1728.000000 | 1406.000000 | 20.304257 | 2.881934 | 706 | SOM | ||
| 114 | Cuba | 3174.000000 | 89.000000 | 2525.000000 | 560.000000 | 28.022491 | 2.804033 | 192 | CUB | ||
| 115 | Cabo Verde | 3073.000000 | 33.000000 | 2232.000000 | 808.000000 | 552.709771 | 1.073869 | 132 | CPV | ||
| 116 | Sri Lanka | 2882.000000 | 11.000000 | 2658.000000 | 213.000000 | 13.458956 | 0.381679 | 144 | LKA | ||
| 117 | Slovakia | 2801.000000 | 31.000000 | 1944.000000 | 826.000000 | 51.303721 | 1.106748 | 703 | SVK | ||
| 118 | Suriname | 2761.000000 | 40.000000 | 1830.000000 | 891.000000 | 470.651207 | 1.448750 | 740 | SUR | ||
| 119 | Mozambique | 2638.000000 | 19.000000 | 1015.000000 | 1604.000000 | 8.440132 | 0.720243 | 508 | MOZ | ||
| 120 | Mali | 2597.000000 | 125.000000 | 1979.000000 | 493.000000 | 12.824163 | 4.813246 | 466 | MLI | ||
| 121 | South Sudan | 2478.000000 | 47.000000 | 1175.000000 | 1256.000000 | 22.137395 | 1.896691 | 728 | SSD | ||
| 122 | Slovenia | 2369.000000 | 129.000000 | 1960.000000 | 280.000000 | 113.952741 | 5.445336 | 705 | SVN | ||
| 123 | Lithuania | 2352.000000 | 81.000000 | 1691.000000 | 580.000000 | 86.397817 | 3.443878 | 440 | LTU | ||
| 124 | Rwanda | 2200.000000 | 8.000000 | 1558.000000 | 634.000000 | 16.985520 | 0.363636 | 646 | RWA | ||
| 125 | Estonia | 2177.000000 | 63.000000 | 1976.000000 | 138.000000 | 164.111270 | 2.893891 | 233 | EST | ||
| 126 | Guinea-Bissau | 2088.000000 | 29.000000 | 1015.000000 | 1044.000000 | 106.097669 | 1.388889 | 624 | GNB | ||
| 127 | Benin | 2014.000000 | 38.000000 | 1681.000000 | 295.000000 | 16.612778 | 1.886792 | 204 | BEN | ||
| 128 | Iceland | 1983.000000 | 10.000000 | 1861.000000 | 112.000000 | 581.098901 | 0.504286 | 352 | ISL | ||
| 129 | Sierra Leone | 1940.000000 | 69.000000 | 1496.000000 | 375.000000 | 24.319966 | 3.556701 | 694 | SLE | ||
| 130 | Yemen | 1847.000000 | 528.000000 | 949.000000 | 370.000000 | 6.192590 | 28.586898 | 887 | YEM | ||
| 131 | Tunisia | 1847.000000 | 53.000000 | 1302.000000 | 492.000000 | 15.627885 | 2.869518 | 788 | TUN | ||
| 132 | Angola | 1815.000000 | 80.000000 | 577.000000 | 1158.000000 | 5.522379 | 4.407713 | 24 | AGO | ||
| 133 | New Zealand | 1602.000000 | 22.000000 | 1531.000000 | 49.000000 | 33.221124 | 1.373283 | 554 | NZL | ||
| 134 | Gambia | 1556.000000 | 43.000000 | 267.000000 | 1246.000000 | 64.386278 | 2.763496 | 270 | GMB | ||
| 135 | Syria | 1432.000000 | 55.000000 | 395.000000 | 982.000000 | 8.182550 | 3.840782 | 760 | SYR | ||
| 136 | Uruguay | 1409.000000 | 37.000000 | 1180.000000 | 192.000000 | 40.561622 | 2.625976 | 858 | URY | ||
| 137 | Uganda | 1353.000000 | 11.000000 | 1141.000000 | 201.000000 | 2.957959 | 0.813008 | 800 | UGA | ||
| 138 | Jordan | 1320.000000 | 11.000000 | 1222.000000 | 87.000000 | 12.937194 | 0.833333 | 400 | JOR | ||
| 139 | Latvia | 1308.000000 | 32.000000 | 1078.000000 | 198.000000 | 69.345701 | 2.446483 | 428 | LVA | ||
| 140 | Georgia | 1306.000000 | 17.000000 | 1085.000000 | 204.000000 | 32.738599 | 1.301685 | 268 | GEO | ||
| 141 | Cyprus | 1305.000000 | 20.000000 | 870.000000 | 415.000000 | 108.086976 | 1.532567 | 196 | CYP | ||
| 142 | Malta | 1276.000000 | 9.000000 | 762.000000 | 505.000000 | 288.989195 | 0.705329 | 470 | MLT | ||
| 143 | Liberia | 1252.000000 | 82.000000 | 738.000000 | 432.000000 | 24.754448 | 6.549521 | 430 | LBR | ||
| 144 | Burkina Faso | 1228.000000 | 54.000000 | 997.000000 | 177.000000 | 5.874677 | 4.397394 | 854 | BFA | ||
| 145 | Botswana | 1214.000000 | 3.000000 | 120.000000 | 1091.000000 | 51.623877 | 0.247117 | 72 | BWA | ||
| 146 | Niger | 1161.000000 | 69.000000 | 1075.000000 | 17.000000 | 4.796205 | 5.943152 | 562 | NER | ||
| 147 | Togo | 1104.000000 | 26.000000 | 791.000000 | 287.000000 | 13.335367 | 2.355072 | 768 | TGO | ||
| 148 | Bahamas | 1089.000000 | 15.000000 | 138.000000 | 936.000000 | 276.924485 | 1.377410 | 44 | BHS | ||
| 149 | Jamaica | 1071.000000 | 14.000000 | 754.000000 | 303.000000 | 36.168246 | 1.307190 | 388 | JAM | ||
| 150 | Andorra | 981.000000 | 53.000000 | 858.000000 | 70.000000 | 1269.656377 | 5.402650 | 20 | AND | ||
| 151 | Chad | 949.000000 | 76.000000 | 860.000000 | 13.000000 | 5.777476 | 8.008430 | 148 | TCD | ||
| 152 | Vietnam | 929.000000 | 21.000000 | 430.000000 | 478.000000 | 0.954401 | 2.260495 | 704 | VNM | ||
| 153 | Lesotho | 884.000000 | 25.000000 | 271.000000 | 588.000000 | 41.264987 | 2.828054 | 426 | LSO | ||
| 154 | Sao Tome and Principe | 883.000000 | 15.000000 | 808.000000 | 60.000000 | 402.900151 | 1.698754 | 678 | STP | ||
| 155 | Diamond Princess | 712.000000 | 13.000000 | 651.000000 | 48.000000 | 1.825843 | 9999 | ||||
| 156 | San Marino | 699.000000 | 42.000000 | 657.000000 | 0.000000 | 2059.638164 | 6.008584 | 674 | SMR | ||
| 157 | Guyana | 631.000000 | 22.000000 | 202.000000 | 407.000000 | 80.222844 | 3.486529 | 328 | GUY | ||
| 158 | Tanzania | 509.000000 | 21.000000 | 183.000000 | 305.000000 | 0.852108 | 4.125737 | 834 | TZA | ||
| 159 | Taiwan* | 481.000000 | 7.000000 | 450.000000 | 24.000000 | 2.019585 | 1.455301 | 158 | TWN | ||
| 160 | Burundi | 410.000000 | 1.000000 | 315.000000 | 94.000000 | 3.448049 | 0.243902 | 108 | BDI | ||
| 161 | Trinidad and Tobago | 404.000000 | 8.000000 | 139.000000 | 257.000000 | 28.867638 | 1.980198 | 780 | TTO | ||
| 162 | Comoros | 399.000000 | 7.000000 | 379.000000 | 13.000000 | 45.883428 | 1.754386 | 174 | COM | ||
| 163 | Burma | 369.000000 | 6.000000 | 321.000000 | 42.000000 | 0.678187 | 1.626016 | 104 | MMR | ||
| 164 | Mauritius | 344.000000 | 10.000000 | 334.000000 | 0.000000 | 27.048980 | 2.906977 | 480 | MUS | ||
| 165 | Mongolia | 297.000000 | 0.000000 | 269.000000 | 28.000000 | 9.059596 | 0.000000 | 496 | MNG | ||
| 166 | Belize | 296.000000 | 2.000000 | 32.000000 | 262.000000 | 74.442748 | 0.675676 | 84 | BLZ | ||
| 167 | Eritrea | 285.000000 | 0.000000 | 248.000000 | 37.000000 | 8.036257 | 0.000000 | 232 | ERI | ||
| 168 | Cambodia | 273.000000 | 0.000000 | 225.000000 | 48.000000 | 1.632876 | 0.000000 | 116 | KHM | ||
| 169 | Papua New Guinea | 271.000000 | 3.000000 | 78.000000 | 190.000000 | 3.028939 | 1.107011 | 598 | PNG | ||
| 170 | Barbados | 144.000000 | 7.000000 | 118.000000 | 19.000000 | 50.109440 | 4.861111 | 52 | BRB | ||
| 171 | Monaco | 144.000000 | 4.000000 | 114.000000 | 26.000000 | 366.935073 | 2.777778 | 492 | MCO | ||
| 172 | Brunei | 142.000000 | 3.000000 | 138.000000 | 1.000000 | 32.458404 | 2.112676 | 96 | BRN | ||
| 173 | Bhutan | 128.000000 | 0.000000 | 100.000000 | 28.000000 | 16.588648 | 0.000000 | 64 | BTN | ||
| 174 | Seychelles | 127.000000 | 0.000000 | 126.000000 | 1.000000 | 129.143787 | 0.000000 | 690 | SYC | ||
| 175 | Antigua and Barbuda | 93.000000 | 3.000000 | 83.000000 | 7.000000 | 94.967731 | 3.225806 | 28 | ATG | ||
| 176 | Liechtenstein | 90.000000 | 1.000000 | 87.000000 | 2.000000 | 235.991295 | 1.111111 | 438 | LIE | ||
| 177 | Saint Vincent and the Grenadines | 57.000000 | 0.000000 | 55.000000 | 2.000000 | 51.375882 | 0.000000 | 670 | VCT | ||
| 178 | Fiji | 28.000000 | 1.000000 | 20.000000 | 7.000000 | 3.123452 | 3.571429 | 242 | FJI | ||
| 179 | Saint Lucia | 25.000000 | 0.000000 | 25.000000 | 0.000000 | 13.614407 | 0.000000 | 662 | LCA | ||
| 180 | Timor-Leste | 25.000000 | 0.000000 | 24.000000 | 1.000000 | 1.896177 | 0.000000 | 626 | TLS | ||
| 181 | Grenada | 24.000000 | 0.000000 | 23.000000 | 1.000000 | 21.329731 | 0.000000 | 308 | GRD | ||
| 182 | Laos | 22.000000 | 0.000000 | 19.000000 | 3.000000 | 0.302382 | 0.000000 | 418 | LAO | ||
| 183 | Dominica | 18.000000 | 0.000000 | 18.000000 | 0.000000 | 25.003125 | 0.000000 | 212 | DMA | ||
| 184 | Saint Kitts and Nevis | 17.000000 | 0.000000 | 17.000000 | 0.000000 | 31.959693 | 0.000000 | 659 | KNA | ||
| 185 | Holy See | 12.000000 | 0.000000 | 12.000000 | 0.000000 | 1483.312732 | 0.000000 | 336 | VAT | ||
| 186 | Western Sahara | 10.000000 | 1.000000 | 8.000000 | 1.000000 | 1.674116 | 10.000000 | 732 | ESH | ||
| 187 | MS Zaandam | 9.000000 | 2.000000 | 7.000000 | 22.222222 | 8888 |
if you see in the above table , united states has the mostnumber of confirmed cases and second is brazil and then third is india whereas in the recovery rate us is number one and india is number 2 .